AI Engineer Job Description: 2026 Template

An AI engineer builds software features on top of large language models, such as retrieval systems, agents, and AI-powered workflows, and keeps them reliable in production. A good job description names the product problem, the models and stack you use, how you measure output quality, and whether the role builds new features or hardens existing ones.

Below is a template you can copy. After it, you will find what US companies are asking for in 2026, how the role differs from machine learning engineering and data science, interview questions written for the interviewer, and a scorecard to compare candidates.

AI engineer job description template

Replace the text in brackets and delete any line that is not true for your team. The title “AI engineer” covers very different jobs, so the more specific the description, the better the applicants.

About the role

We are looking for a [Senior] AI Engineer to design, build, and run the LLM-powered features behind [product]. You will own [retrieval, agent workflows, or AI features] from prototype to production, working with [models or providers] on a [Python or TypeScript] stack. You will report to [role] and work with [product, engineering, and domain experts].

Responsibilities

• Build and ship LLM-powered features such as [search, assistants, summarization, or agents] to production.
• Design retrieval pipelines with [pgvector, Pinecone, or similar], including chunking, embeddings, and ranking.
• Build agent workflows with tool calling, structured outputs, and [MCP or internal APIs].
• Create evaluation sets and automated evals to measure quality before and after every change.
• Monitor cost, latency, and failure rates in production, and improve them over time.
• Apply guardrails for privacy and security, including prompt-injection defenses and [HIPAA, SOC 2, or other] requirements.
• Work with product managers and domain experts to define what a good output looks like.

Must-have qualifications

• [5]+ years of software engineering experience, including [1 to 2] years building LLM features in production.
• Strong Python or TypeScript.
• Hands-on experience with LLM APIs such as [OpenAI, Anthropic, or Google].
• Experience building retrieval-augmented generation (RAG) systems with vector databases.
• Experience designing evaluations for model outputs.
• Solid backend fundamentals: APIs, databases, testing, and deployment.
• Clear written communication in English.

Nice to have

• Agent frameworks such as [LangGraph, the OpenAI Agents SDK, the Claude Agent SDK, or the Vercel AI SDK].
• Building or running Model Context Protocol (MCP) servers.
• Evaluation and observability tools such as [LangSmith, Braintrust, or Langfuse].
• Fine-tuning experience, and the judgment to know when it is not needed.
• Durable workflow tools such as [Temporal or Inngest].
• Experience with [your industry or regulated data].

What we offer

[Compensation range, if you can share it], [location or time zone requirements], [benefits], and [how the team works day to day].

What US companies ask for in AI engineers in 2026

We reviewed the requirements in the engineering roles US companies asked BEON.tech to fill between 2024 and 2026, including roles titled AI engineer and other engineering roles that ask for AI skills. These are the patterns that stand out:

• AI requirements went from rare to common. About one in twenty engineering roles mentioned AI in 2024, either building with LLMs or using AI coding tools. In 2026, about four in ten do.
• Most LLM work is hired under other titles. Of the 2025 and 2026 roles that asked for hands-on LLM experience, only about one in six was titled AI engineer. More than half were backend or full-stack roles.
• Retrieval is the most requested skill. Close to half of those roles ask for RAG, vector databases, or semantic search, with pgvector, Pinecone, and Qdrant among the tools named.
• Agents and MCP moved to the center. Recent roles ask for agent frameworks, tool calling, and structured outputs. Model Context Protocol first appeared in the requirements in 2026.
• Production experience is the bar. More than half of the roles stress shipping AI features to production, and several say plainly that prototypes or courses are not enough.
• Evaluation is becoming a named skill. 2026 roles name tools such as LangSmith, Braintrust, and Langfuse, and ask for evaluation frameworks for outputs that are hard to score.
• Fine-tuning is moving to the nice-to-have list. Earlier AI engineer roles listed it as a core skill. Recent roles list it more often as a bonus, and one asked for the judgment to know when not to fine-tune.
• Python leads, and TypeScript is common. Python appears in more than half of these roles. About a third ask for TypeScript, usually for AI features inside full-stack products.
• Seniority is measured in software engineering years. Most AI engineer roles asked for five or more years of software engineering experience, not years in AI.

The takeaway for your description: decide first whether you need a dedicated AI engineer or a senior backend or full-stack engineer who can ship LLM features. Then ask for retrieval, evaluation, and production experience, and treat model training as a bonus unless you train your own models.

AI engineer vs. machine learning engineer vs. data scientist

These titles are often used interchangeably, which is why many AI engineer postings attract the wrong applicants. Choose the one that matches the work.

Role	Main focus	Choose it when
AI engineer	Builds product features on top of existing models: retrieval, agents, and evaluation.	AI features are central to the product and need a dedicated owner.
Machine learning engineer	Trains, deploys, and monitors models, including custom ones.	You train your own models or run ML pipelines at scale.
Data scientist	Analyzes data and builds models to answer business questions.	The main need is insight and experimentation, not shipping features.
Backend or full-stack engineer with LLM experience	Adds AI features to an existing product alongside regular product work.	AI is one part of the roadmap, and most of the work is standard product engineering.

If the engineer will work directly inside your customers’ environments to deploy AI solutions, the role is closer to a forward deployed engineer. See how to hire a forward deployed engineer.

How the role changes by seniority

Level	Scope	What to look for in the interview
Mid-level (3 to 5 years)	Builds AI features within an existing architecture and evaluation setup.	Clean code, a working RAG or agent project, and honest answers about what failed.
Senior (5 to 8 years)	Designs retrieval, agent, and evaluation systems end to end and owns quality in production.	Trade-offs among cost, latency, and quality, and evaluations they designed.
Lead or staff (8+ years)	Sets the AI architecture, model and vendor choices, and guardrails across teams.	Build-versus-buy decisions, and how they set quality standards for other engineers.

AI engineer interview questions for the interviewer

These questions are written for the person running the interview. Each one lists what a strong answer covers and a warning sign. There are no model answers to memorize. The goal is to hear how the candidate reasons about systems that are never fully predictable.

Retrieval and output quality

1. “Walk me through a RAG system you shipped. What did you change after real users started using it?” A strong answer covers chunking, embeddings, how retrieval quality was measured, and changes driven by real queries, such as reranking or better metadata. Warning sign: they cannot say how they measured retrieval quality.
2. “The assistant passes your tests, but users say it is often wrong. Where do you look?” Listen for sampling real traffic, separating retrieval failures from generation failures, and turning those failures into new test cases. Warning sign: switching to a bigger model first.

Agents and tool calling

1. “When would you build an agent, and when is a fixed workflow better?” A strong answer reserves agents for tasks whose steps cannot be known in advance and prefers deterministic flows and structured outputs everywhere else. Warning sign: agents by default.
2. “How do you stop an agent from taking a harmful or expensive action?” Listen for permissions scoped per tool, confirmation steps for risky actions, budgets and timeouts, logging, and defenses against prompt injection. Warning sign: relying on instructions in the prompt alone.

Evaluation and production

1. “How do you know a prompt or model change made things better?” A strong answer describes versioned evaluation sets, automated scoring plus human review, regression checks before release, and metrics from production. Warning sign: “I tried a few examples and it looked better.”
2. “Your AI feature costs three times its budget. What do you do?” Listen for measuring cost per request, caching, smaller models for simpler steps, and shorter context. Warning sign: cutting quality without measuring the impact.
3. “When would you fine-tune a model instead of improving prompts or retrieval?” A strong answer saves fine-tuning for narrow, stable tasks with enough good data, after cheaper options are exhausted, and weighs the maintenance cost. Warning sign: fine-tuning as the first answer. For background, see RAG vs. fine-tuning vs. agents.

Working with AI coding tools

1. “How do you use AI coding assistants in your own work, and how do you review what they produce?” A strong answer describes a clear workflow with tests and review, and names the kinds of changes they never accept without reading closely. Warning sign: no verification step. For a fuller framework, see how to evaluate engineers who work with AI agents.

Collaboration

1. “How would you explain to a non-technical executive why the assistant will never be 100 percent accurate, and what you will do about it?” Look for measurable quality targets, human review for high-risk cases, and a clear plan for handling errors. Warning sign: overpromising or hiding behind jargon.

A practical exercise

Give candidates a small set of documents and 20 to 30 realistic questions, including some the documents cannot answer. Ask them to build a simple retrieval-based assistant, write an evaluation that scores the answers and catches cases where the assistant should say it does not know, and explain what they would improve next. Provide API keys with a spending cap, allow AI coding tools, and ask what they checked before trusting the output. Keep a take-home under four hours, or run a shorter version live in 90 minutes.

AI engineer interview scorecard

Score each criterion from 1 (no evidence) to 4 (strong evidence). Agree on the weights before the first interview, not after.

Criterion	What a 4 looks like	Suggested weight
LLM application design	Designs retrieval and agent systems that fit the problem, with structured outputs and clear limits.	25%
Evaluation and quality	Builds evaluation sets, measures changes, and turns production failures into tests.	20%
Software engineering fundamentals	Writes tested, maintainable code and reviews AI-generated code carefully.	20%
Production: cost, latency, and reliability	Has run AI features in production and improved cost and latency with data.	15%
Safety and data handling	Scopes permissions, defends against prompt injection, and handles sensitive data correctly.	10%
Communication	Explains what AI can and cannot do to product and non-technical stakeholders.	10%

Hiring an AI engineer through BEON.tech

If you would rather skip sourcing and screening, BEON.tech places senior AI engineers from Latin America who work in US time zones. For the full process, read our guide on how to hire AI engineers or see how we help companies hire senior AI engineers.

FAQ

What does an AI engineer do?

An AI engineer builds software features on top of large language models and other AI models. The work includes retrieval systems, agents, integrations with model APIs, evaluation of output quality, and monitoring cost and reliability in production. Most AI engineers use existing models rather than training new ones, which is the main difference from machine learning engineers.

What should an AI engineer job description include?

Include the product problem the role will solve, the main responsibilities, your models and stack, must-have and nice-to-have qualifications, and what you offer. Be specific about retrieval, agents, and evaluation, say whether the engineer will train models, and name any compliance requirements for the data the system will handle.

What is the difference between an AI engineer and a machine learning engineer?

An AI engineer builds product features on top of existing models, such as assistants, retrieval systems, and agents. A machine learning engineer trains, deploys, and monitors models, including custom ones, and works closer to data pipelines and training infrastructure. If you do not plan to train your own models, you most likely need an AI engineer.

How many years of experience should a senior AI engineer have?

Most senior AI engineer roles ask for five or more years of software engineering experience. In the AI engineer roles US companies asked BEON.tech to fill, requirements ranged from four to eight years, usually measured in software engineering rather than AI. Because LLM tools are recent, look for strong engineering fundamentals plus production LLM work.